Car-following behavior modeling is critical for understanding traffic flow dynamics and developing high-fidelity microscopic simulation models. Most existing impulse-response car-following models prioritize computational efficiency and interpretability by using a parsimonious nonlinear function based on immediate preceding state observations. However, this approach disregards historical information, limiting its ability to explain real-world driving data. Consequently, serially correlated residuals are commonly observed when calibrating these models with actual trajectory data, hindering their ability to capture complex and stochastic phenomena. To address this limitation, we propose a dynamic regression framework incorporating time series models, such as autoregressive processes, to capture error dynamics. This statistically rigorous calibration outperforms the simple assumption of independent errors and enables more accurate simulation and prediction by leveraging higher-order historical information. We validate the effectiveness of our framework using HighD and OpenACC data, demonstrating improved probabilistic simulations. In summary, our framework preserves the parsimonious nature of traditional car-following models while offering enhanced probabilistic simulations.

翻译：跟驰行为建模对于理解交通流动力学和开发高保真微观仿真模型至关重要。现有大多数脉冲响应类跟驰模型通过基于即时前车状态观测的简约非线性函数，优先考虑计算效率和可解释性。然而，这种方法忽视了历史信息，限制了其解释真实驾驶数据的能力。因此，在利用实际轨迹数据校准这些模型时，常观测到序列相关残差，阻碍了模型对复杂随机现象的捕捉。为解决这一局限，我们提出一种动态回归框架，通过纳入自回归过程等时间序列模型来刻画误差动态。这一统计严谨的校准方法优于简单的独立误差假设，并能利用高阶历史信息实现更精确的仿真与预测。我们利用HighD和OpenACC数据集验证了该框架的有效性，展示了改进的概率仿真效果。总之，本框架在保留传统跟驰模型简约特性的同时，提供了增强的概率仿真能力。